Using Dissertations to Examine Potential Bias in Child and Adolescent Clinical Trials.

AbstractMitochondrial diseases are predominantly caused by mutations of mitochondrial or nuclear DNA, resulting in multisystem defects. Current treatments are largely supportive, and the disorders progress relentlessly. Nutritional supplements, pharmacological agents and physical therapies have been used in different clinical trials, but the efficacy of these interventions need to be further evaluated. Several recent reviews discussed some of the interventions but ignored bias in those trials. This review was conducted to discover new studies and grade the original studies for potential bias with revised Cochrane Collaboration guidelines. We focused on seven published studies and three unpublished studies; eight of these studies showed improvement in outcome measurements. In particular, two of the interventions have been tested in studies with strict design, which we believe deserve further clinical trials with a large sample. Additionally, allotopic expression of the ND4 subunit seemed to be an effective new treatment for patients with Leber hereditary optic neuropathy.

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Failure to address potential bias in non-randomised controlled clinical trials may cause lack of evidence on patient-reported outcomes: a method study

BMJ Open ◽

10.1136/bmjopen-2013-004720 ◽

2014 ◽

Vol 4 (6) ◽

pp. e004720-e004720 ◽

Cited By ~ 7

Author(s):

F. Peinemann ◽

A. M. Labeit ◽

C. Thielscher ◽

M. Pinkawa

Keyword(s):

Clinical Trials ◽

Patient Reported Outcomes ◽

Potential Bias ◽

Controlled Clinical Trials ◽

Patient Reported ◽

Randomised Controlled

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Bayesian analysis of capture-recapture model and diagnostic test in clinical trials

10.32469/10355/48229 ◽

2014 ◽

Author(s):

◽

Dan Zheng

Keyword(s):

Clinical Trials ◽

Simulation Study ◽

Behavioral Response ◽

Bayesian Method ◽

Original Method ◽

Potential Bias ◽

The Em Algorithm ◽

Em Method ◽

Capture Recapture ◽

Simulation Results

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Capture-recapture models have been widely used to estimate the size of a target wildlife population. There are three major sources of variations that can affect capture probabilities: time (i.e., capture probabilities vary with time or trapping occasion), behavioral response (i.e., capture probabilities vary due to a trap response of animals to the first capture), and heterogeneity (i.e., capture probabilities vary by individual animal). There are eight models regarding possible combinations of these factors, including M0, Mt, Mb, Mh, Mtb, Mth, Mbh, and Mtbh. A capture-recapture model (Mb model) was created to present the behavioral response effect. The objective Bayesian analysis for the population size was developed and compared with common maximum likelihood estimates (MLEs). Simulation results demonstrate the advantages of the objective Bayesian over MLEs. Two real examples about a deer mouse are presented and one R package (OBMbpkg) was built for application. Companion diagnostics (CDx) for personalized medicine is commonly applied to in vitro diagnostic (IVD) industry and clinical trials for specific disease or treatment with biomarkers (e.g. molecular targets). The Bayesian method with Gibbs sampler was used to estimate the potential bias caused by imperfect CDx under the targeted design, where only patients with a positive diagnosis were enrolled the clinical trials. A simulation study was conducted to evaluate the performance of the Bayesian method and to compare with the EM algorithm. The Bayesian model selection method with G-prior was used to test treatment effects of targeted drugs for patients with biomarkers under the targeted design. A simulation study was conducted to evaluate the performance of the Bayesian method and to compare it with the original method and EM method when sample size is small. Eventually a biomarker-stratified design was studied, while patients enrolled in clinical trials could be divided into two groups (i.e., those with a positive or negative diagnosis). Both the EM algorithm and Bayesian method were used to estimate the potential bias caused by imperfect CDx. Simulation results demonstrate the advantages of the Bayesian method over the original method and EM method.

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PREMATURE DISCONTINUATION OF PATIENTS MOST AFFECTED BY THE DISEASE UNDER STUDY: A POTENTIAL BIAS IN COPD CLINICAL TRIALS

CHEST Journal ◽

10.1378/chest.130.4_meetingabstracts.179s-a ◽

2006 ◽

Vol 130 (4) ◽

pp. 179S

Author(s):

Steven Kesten ◽

Mark Plautz ◽

Craig Piquette ◽

Michael Habib ◽

Dennis E. Niewoehner

Keyword(s):

Clinical Trials ◽

Potential Bias

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Blinding in randomised clinical trials of psychological interventions: a retrospective study of published trial reports

BMJ evidence-based medicine ◽

10.1136/bmjebm-2020-111407 ◽

2020 ◽

pp. bmjebm-2020-111407

Author(s):

Sophie Juul ◽

Christian Gluud ◽

Sebastian Simonsen ◽

Frederik Weischer Frandsen ◽

Irving Kirsch ◽

...

Keyword(s):

Clinical Trials ◽

Retrospective Study ◽

Null Hypothesis ◽

Primary Outcome ◽

Randomised Clinical Trials ◽

Psychological Interventions ◽

Potential Bias ◽

Treatment Providers ◽

Monitoring Committee ◽

Bias Risk

ObjectivesTo study the extent of blinding in randomised clinical trials of psychological interventions and the interpretative considerations if randomised clinical trials are not blinded.DesignRetrospective study of trial reports published in six high impact factor journals within the field of psychiatry in 2017 and 2018.SettingTrial reports published in World Psychiatry, JAMA Psychiatry, Lancet Psychiatry, American Journal of Psychiatry, British Journal of Psychiatry, or Psychotherapy and Psychosomatics.Main outcome measuresBlinding status of participants, treatment providers, outcome assessors, data managers, the data safety and monitoring committee, statisticians and conclusion makers, if trialists rejected the null hypothesis on the primary outcome measure, and if trialists discussed the potential bias risk from lack of blinding in the published trial report.Results63 randomised clinical trials of psychological interventions were identified. None (0%; 95% CI 0% to 5.75%) of the trials reported blinding of all possible key persons. 37 (58.7%; 95% CI 46.42% to 70.04%) trials reported blinding of outcome assessors. Two (3.2%; 95% CI 0.87% to 10.86%) trials reported blinding of participants. Two (3.2%; 95% CI 0.87% to 10.86%) trials reported blinding of data managers. Three (4.8%; 95% CI 1.63% to 13.09%) trials reported blinding of statisticians. None of the trials reported blinding of treatment providers, the data safety and monitoring committee, and conclusion makers. 45 (71.4%; 95% CI 59.30% to 81.10%) trials rejected the null hypothesis on the primary outcome(s). 13 (20.7%; 95% CI 12.48% to 32.17%) trials discussed the potential bias risk from lack of blinding in the published trial report.ConclusionsBlinding of key persons involved in randomised clinical trials of psychological interventions is rarely sufficiently documented. The possible interpretative limitations are only rarely considered. There is a need of randomised clinical trials of psychological interventions with documented blinding attempts of all possible key persons.

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Premature discontinuation of patients: a potential bias in COPD clinical trials

European Respiratory Journal ◽

10.1183/09031936.00104606 ◽

2007 ◽

Vol 30 (5) ◽

pp. 898-906 ◽

Cited By ~ 25

Author(s):

S. Kesten ◽

M. Plautz ◽

C. A. Piquette ◽

M. P. Habib ◽

D. E. Niewoehner

Keyword(s):

Clinical Trials ◽

Potential Bias

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A potential bias in safety evaluation during open-label extensions of randomized clinical trials

Pharmacoepidemiology and Drug Safety ◽

10.1002/pds.943 ◽

2004 ◽

Vol 13 (5) ◽

pp. 295-298 ◽

Cited By ~ 8

Author(s):

Kenneth J. Rothman

Keyword(s):

Clinical Trials ◽

Randomized Clinical Trials ◽

Safety Evaluation ◽

Potential Bias ◽

Open Label

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Effect of right censoring bias on survival analysis.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e18188 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e18188-e18188

Author(s):

Enrique Barrajon ◽

Laura Barrajon

Keyword(s):

Clinical Trials ◽

Survival Analysis ◽

Survival Time ◽

Interval Censoring ◽

Time Variable ◽

Right Censoring ◽

Potential Bias ◽

Event Time ◽

Bias Index ◽

Off Time

e18188 Background: Survival Kaplan-Meier analysis represents the most objective measure of treatment efficacy in oncology, though subjected to potential bias which is worrisome in an era of precision medicine. Independent of the bias inherent to the design of clinical trials, bias may be the result of patient censoring, or incomplete observation. Unlike disease/progression free survival, overall survival is based on a well defined time point and thus avoids interval censoring, but it is our claim that right censoring, due to incomplete follow-up, may still be a source of bias. Methods: The R version 3.5.1 language and the integrated development environment RStudio were used for simulations and survival analysis with the survival package and their available datasets . Survival time was simulated according to a Weibull model with 2 parameters, shape and scale, that determine the event time for every case. Three types of right censoring mechanisms are considered and analyzed independently: 1) case censoring, in which a random number of cases are censored, and the resulting survival time is shortened by a random amount, 2) time censoring, in which a random censoring time variable is applied if and only if it is shorter than the event time, and 3) interim censoring, where a random time variable determines the case inclusion time since the start of trial, and a fixed cutt-off time determines if every case is censored (if the cutt-off time is shorter the the inclusion time plus the event time) or not. For every censoring mechanism, 100 trials was simulated with a 1000 uncensored cases arm and 1000 censored cases arm, in such a way that a censoring Cox hazard ratio (cHR) may be estimated for every trial. An interactive app showing the right censoring effect is presented. Results: A bias index (BI) was buit based on the survival time of event and censored cases. Case censoring was associated with higher BI (mean = 1.75, SD = 0.29) than time censoring (mean = 1.15, SD = 0.19, p = 2.02e-30) and interim censoring (mean = 0.72, SD = 0.21, p = 3.46e-34). It was found an inverse relationship between the censoring proportion and the cHR in case censoring (r = -0.86). Of all the available datasets, the Veterans' Administration Lung Cancer study showed a bias of 1.83, suggesting case censoring bias in both treatment arms. Conclusions: Based in the results of this study it is suggested that: 1) Final results should include all the events in the defined period of interest, 2) a bias index may help in detecting potential bias and correct estimated survival. Censoring bias analysis is planned in recent clinical trials.

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